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Average Ratings 0 Ratings
Description
Relay Tripping Curves PRO2 (RTC2) is a Windows-based application developed by protection engineers specifically for their peers in the field. It enables users to plot trip curves for relays, circuit breakers, and transformers on a log-log graph, ensuring the selectivity between different devices is verified, while also producing comprehensive technical reports necessary for professional engineering tasks.
Field testing equipment may provide measurements, but they often fail to contextualize these readings with the primary system, visualize the curves, or document that settings are indeed coordinating, a gap that RTC2 effectively addresses.
Key features include support for seven relay manufacturers, each replicating the original display and terminology, ten circuit breaker trip units encompassing L/S/I/G zones, and a transformer damage curve compliant with ANSI/IEEE C57.109-2018 standards, as well as capabilities for inrush and short-circuit current analysis, ΔT selectivity calculations among devices, and the generation of PDF reports that feature primary-equivalent current, curve equations, plots, and signatures. Moreover, the software is fully bilingual in English and Spanish, and it operates entirely offline.
Users can explore a free DEMO version that has no time restrictions, while paid editions offer a perpetual license for a one-time payment. This flexibility ensures that all engineers can find a suitable option tailored to their needs.
Description
ndCurveMaster, a specialized curve fitting software, is designed to fit curves with multiple variables. It automatically applies nonlinear equations to your datasets. These can be observed or measured values. The software supports curve and surfaces fitting in 2D 3D 4D 5D ..., dimensions. ndCurveMaster is able to handle any data, no matter how complex or how many variables there are.
ndCurveMaster, for example, can efficiently derive the optimal equations for a dataset that has six inputs (x1-x6) and a corresponding output Y. For example: Y = a0 - a1 - exp(x1)0.5 + a2 ln(x2)8... + a6 x65.2 to accurately match measured value.
ndCurveMaster uses machine learning numerical methods to automatically fit the most suitable nonlinear regression function to your dataset, and discover the relationships between inputs and outputs. This tool supports various curve fitting methods, including linear, polynomial, and nonlinear methods. It also utilizes essential validation and goodness-of-fit tests to ensure accuracy. Additionally, ndCurveMaster provides advanced assessments, such as detecting overfitting and multicollinearity, using tools like the Variance Inflation Factor (VIF) and the Pearson correlation matrix.
API Access
Has API
API Access
Has API
Screenshots View All
No images available
Integrations
Pascal
Python
Pricing Details
Free demo
Free Trial
Free Version
Pricing Details
€289
Free Trial
Free Version
Deployment
Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook
Deployment
Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook
Customer Support
Business Hours
Live Rep (24/7)
Online Support
Customer Support
Business Hours
Live Rep (24/7)
Online Support
Types of Training
Training Docs
Webinars
Live Training (Online)
In Person
Types of Training
Training Docs
Webinars
Live Training (Online)
In Person
Vendor Details
Company Name
PROTELECTSA
Founded
2024
Country
Panama
Website
www.protelectsa.net
Vendor Details
Company Name
SigmaLab Tomas Cepowski
Founded
2017
Country
Poland
Website
www.ndcurvemaster.com
Product Features
Electrical Design
CAD Tools
Change Management
Collaboration
Compliance Management
Document Generation
Drag & Drop
Electrical Parts Catalog
Functions / Calculations
One Line Diagram
PLC Tools
Reusable Designs
Symbol Library
Product Features
Statistical Analysis
Analytics
Association Discovery
Compliance Tracking
File Management
File Storage
Forecasting
Multivariate Analysis
Regression Analysis
Statistical Process Control
Statistical Simulation
Survival Analysis
Time Series
Visualization